How can policy gradients be applied in the case of multiple continuous actions?
As you has said, actions chosen by Actor-Critic typically come from a normal distribution and it is the agent's job to find the appropriate mean and standard deviation based on the the current state. In many cases this one distribution is enough because only 1 continuous action is required. However, as domains such as robotics become more integrated with AI, situations where 2 or more continuous actions are required are a growing problem.

There are 2 solutions to this problem:
The first and most common is that for every continuous action, there is a separate agent learning its own 1-dimensional mean and standard deviation. Part of its state includes the actions of the other agents as well to give context of what the entire system is doing. We commonly do this in my lab and here is a paper which describes this approach with 3 actor-critic agents working together to move a robotic arm. 

The second approach is to have one agent find a multivariate (usually normal) distribution of a policy. Although in theory, this approach could have a more concise policy distribution by "rotating" the distribution based on the co-variance matrix, it means that all of the values of the co-variance matrix must be learned as well. This increases the number of values that must be learned to have $n$ continuous outputs from $2n$ (mean and stddev), to $n+n^2$ ($n$ means and an $n \times n$ co-variance matrix). This drawback has made this approach not as popular in the literature.

This is a more general answer but should help you and others on their related problems.